Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

Atmospheric Propagation Of SubTHz Waves And Space, 6G & 7G Communications

The work is devoted to the actual problem of data rate of wireless telecommunication channels. Presented analysis of the telecommunication channel subterahertz (subTHz) frequency range - as the most promising band for the implementation of wireless telecommunications for space links and terrestrial cellular communications of high capacity. A channel considered as a combination of high effective transponder / transmitter duplex together with an open high dissipative atmospheric line. The means to achieve a high signal / noise ratio is usage of low-noise cryogenic receivers. The theoretical analysis of data rates for various atmospheric conditions and technical implementations of communication channels demonstrated reasonable limits of cooling of receivers, providing a weighty increase channel capacity, while deeper cooling is impractical due to weather restrictions in certain ranges and conditions of signal propagation, including altitude and seasonal features.

Advanced procedure noise model validation using Seattle International Airport noise monitor networks

Advanced operational flight procedures that utilize modifications to thrust, airspeed, altitude, and configuration can be implemented to mitigate noise impacts for communities surrounding airports. Evaluating and designing such procedures requires accurate modeling of the aircraft performance, source noise, and atmospheric propagation of the source noise to the ground. Modeling frameworks to assess advanced procedures have been developed but must be validated to ensure their results are reasonable. This paper presents validation of such noise models using a network of ground noise monitoring data at Seattle-Tacoma International airport and ADS-B operational radar flight profiles from the OpenSky database. Modeled noise from operational flights of several aircraft types are shown to be consistent with noise monitor data when reasonable flap settings and atmospheric corrections for the actual weather at the time of flight are used. Discrepancies that exist between the modeled and measured noise results are identified to determine where current noise modeling methods must be improved to accurately represent all relevant noise sources.

Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications

Atmospheric effects have a significant impact on the performance of airborne and space laser systems. Traditional methods used to predict propagation effects rely heavily on simplified assumptions of the atmospheric properties and of the interactions with the laser systems. These models need to be continually improved to develop high-resolution predictors of laser performance for applications including LIDAR (light detection and ranging), free-space optical communications, remote sensing, etc. The underlying causes of laser beam attenuation in the atmosphere are examined with particular focus on dominant linear effects: absorption, scattering, turbulence, and non-linear thermal effects such as blooming, kinetic cooling, and bleaching. These phenomena are quantitatively analyzed, highlighting the key assumptions made in the empirical modelling. Absorption and scattering, as the dominant causes of attenuation, are generally well applied in models, but the impact of non-linear phenomena is less well captured and applied as it tends to be application specific. Atmospheric radiative transfer codes, such as MODTRAN, ARTS, etc., and the associated spectral databases, such as HITRAN, are the effective implementation of the total propagative effects on the laser systems. These codes are powerful, widely used tools to analyze performance. However, atmospheric radiative transfer codes make several assumptions that reduce accuracy in favor of faster processing. The key atmospheric radiative transfer models are reviewed highlighting the associated methodologies, assumptions, and application. Empirical models are found to offer a robust analysis of atmospheric propagation, which is particularly well-suited for design, development, test and evaluation (DDT&E) purposes. As such, empirical, semi-empirical, and ensemble methodologies are suggested to compliment and augment the existing atmospheric radiative transfer codes. There is scope to evolve the numerical codes and empirical approaches to better suit aerospace applications, where fast analysis is required over a range of slant paths, incidence angles, altitudes, and atmospheric properties, which are not exhaustively captured in current quantitative performance assessment methods.

Integrated Water Vapor during Rain and Rain-Free Conditions above the Swiss Plateau

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods.

Security and Communication Distance Improvement in Decoy States Based Quantum Key Distribution Using Pseudo-Random Bases Choice for Photon Polarization Measurement

This paper proposes a new quantum key distribution(QKD) protocol, namely the pseudo-random bases entangled photon based QKD (PRB-EPQKD) protocol. The latest mainly focuses on three properties, including the security of the protocol, the secure key size and the maximum communication distance between legitimate communication users (Alice and Bob). To achieve this, we first consider a spontaneous-parametric-down (SPDC) photon source located in a low-earth-orbit (LEO) type satellite capable of producing and distributing entangled photons pairs to Alice and Bob. Secondly, we assume that Alice's and Bob's photons state measurement bases are identically generated via a pseudo-random number generator (PRNG), namely the quantum logistic map (QLM). Finally, we also assume that in addition to their photons states, Alice and Bob intentionally share a set of decoy states at each pulse with randomly selected intensity, and with the goal to detect the presence of the eavesdropper (Eve). Under these considerations, the secure key rate upper bound is evaluated applying the Gottesman-Lo-Lutkenhaus-Preskill's (GLLP) formula, for two different implementations, namely the non-decoy states and the infinite active decoy states based QKD. It is observed a significant improvement in the secure key size and the communication distance as well, compared to existing protocols, since we realize that under daylight, downlinks satellite conditions, a kindly selected light source, and good crystal's properties, the maximum communication distance can reach up to 70000 km. In addition, using the combined type-I and type-II SPDC photons source as our entangled photons pairs generator, significantly improved the photon mean number and render our protocol more robust against photon number division attack and against attenuation-induced atmospheric propagation. Furthermore, the protocol is more secure as compared to existing ones, given that any eavesdropper must crack simultaneously the chaotic system used as PRNG and the QKD system, before getting any useful information as regards to the measurement bases used by Alice and Bob, and thus the secure key.

Deep Learning for Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series

<p>Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. <br>However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. <br>We show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour.<br>Applied to InSAR data over the North Anatolian Fault, our method reaches  2 mm detection, revealing a slow earthquake twice as extensive as previously recognized.<br>We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California. </p>